import argparse import itertools import math import os import datetime import logging import json from pathlib import Path import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import LoggerType, set_seed from diffusers import AutoencoderKL, DDPMScheduler, EulerAncestralDiscreteScheduler, UNet2DConditionModel from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup from PIL import Image from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from slugify import slugify from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion from data.csv import CSVDataModule from training.optimization import get_one_cycle_schedule from models.clip.prompt import PromptProcessor logger = get_logger(__name__) torch.backends.cuda.matmul.allow_tf32 = True def parse_args(): parser = argparse.ArgumentParser( description="Simple example of a training script." ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--train_data_file", type=str, default=None, help="A CSV file containing the training data." ) parser.add_argument( "--instance_identifier", type=str, default=None, help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--class_identifier", type=str, default=None, help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--placeholder_token", type=str, nargs='*', help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--initializer_token", type=str, nargs='*', help="A token to use as initializer word." ) parser.add_argument( "--num_class_images", type=int, default=400, help="How many class images to generate." ) parser.add_argument( "--repeats", type=int, default=1, help="How many times to repeat the training data." ) parser.add_argument( "--output_dir", type=str, default="output/text-inversion", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" " process." ), ) parser.add_argument( "--num_train_epochs", type=int, default=100 ) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=True, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="one_cycle", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup", "one_cycle"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=300, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--lr_cycles", type=int, default=None, help="Number of restart cycles in the lr scheduler." ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer." ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer." ) parser.add_argument( "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use." ) parser.add_argument( "--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer" ) parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU." ), ) parser.add_argument( "--checkpoint_frequency", type=int, default=500, help="How often to save a checkpoint and sample image", ) parser.add_argument( "--sample_frequency", type=int, default=100, help="How often to save a checkpoint and sample image", ) parser.add_argument( "--sample_image_size", type=int, default=512, help="Size of sample images", ) parser.add_argument( "--sample_batches", type=int, default=1, help="Number of sample batches to generate per checkpoint", ) parser.add_argument( "--sample_batch_size", type=int, default=1, help="Number of samples to generate per batch", ) parser.add_argument( "--valid_set_size", type=int, default=None, help="Number of images in the validation dataset." ) parser.add_argument( "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader." ) parser.add_argument( "--sample_steps", type=int, default=30, help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", ) parser.add_argument( "--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss." ) parser.add_argument( "--noise_timesteps", type=int, default=1000, ) parser.add_argument( "--resume_from", type=str, default=None, help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)" ) parser.add_argument( "--resume_checkpoint", type=str, default=None, help="Path to a specific checkpoint to resume training from (ie, logs/token_name/2022-09-22T23-36-27/checkpoints/something.bin)." ) parser.add_argument( "--config", type=str, default=None, help="Path to a JSON configuration file containing arguments for invoking this script. If resume_from is given, its resume.json takes priority over this." ) args = parser.parse_args() if args.resume_from is not None: with open(f"{args.resume_from}/resume.json", 'rt') as f: args = parser.parse_args( namespace=argparse.Namespace(**json.load(f)["args"])) elif args.config is not None: with open(args.config, 'rt') as f: args = parser.parse_args( namespace=argparse.Namespace(**json.load(f)["args"])) if args.train_data_file is None: raise ValueError("You must specify --train_data_file") if args.pretrained_model_name_or_path is None: raise ValueError("You must specify --pretrained_model_name_or_path") if isinstance(args.initializer_token, str): args.initializer_token = [args.initializer_token] if len(args.initializer_token) == 0: raise ValueError("You must specify --initializer_token") if isinstance(args.placeholder_token, str): args.placeholder_token = [args.placeholder_token] if len(args.placeholder_token) == 0: args.placeholder_token = [f"<*{i}>" for i in range(args.initializer_token)] if len(args.placeholder_token) != len(args.initializer_token): raise ValueError("You must specify --placeholder_token") if args.output_dir is None: raise ValueError("You must specify --output_dir") return args def freeze_params(params): for param in params: param.requires_grad = False def save_args(basepath: Path, args, extra={}): info = {"args": vars(args)} info["args"].update(extra) with open(basepath.joinpath("args.json"), "w") as f: json.dump(info, f, indent=4) def make_grid(images, rows, cols): w, h = images[0].size grid = Image.new('RGB', size=(cols*w, rows*h)) for i, image in enumerate(images): grid.paste(image, box=(i % cols*w, i//cols*h)) return grid class Checkpointer: def __init__( self, datamodule, accelerator, vae, unet, tokenizer, text_encoder, instance_identifier, placeholder_token, placeholder_token_id, output_dir: Path, sample_image_size, sample_batches, sample_batch_size, seed ): self.datamodule = datamodule self.accelerator = accelerator self.vae = vae self.unet = unet self.tokenizer = tokenizer self.text_encoder = text_encoder self.instance_identifier = instance_identifier self.placeholder_token = placeholder_token self.placeholder_token_id = placeholder_token_id self.output_dir = output_dir self.sample_image_size = sample_image_size self.seed = seed or torch.random.seed() self.sample_batches = sample_batches self.sample_batch_size = sample_batch_size @torch.no_grad() def checkpoint(self, step, postfix): print("Saving checkpoint for step %d..." % step) checkpoints_path = self.output_dir.joinpath("checkpoints") checkpoints_path.mkdir(parents=True, exist_ok=True) unwrapped = self.accelerator.unwrap_model(self.text_encoder) for (placeholder_token, placeholder_token_id) in zip(self.placeholder_token, self.placeholder_token_id): # Save a checkpoint learned_embeds = unwrapped.get_input_embeddings().weight[placeholder_token_id] learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()} filename = f"%s_%d_%s.bin" % (slugify(placeholder_token), step, postfix) torch.save(learned_embeds_dict, checkpoints_path.joinpath(filename)) del unwrapped del learned_embeds @torch.no_grad() def save_samples(self, step, height, width, guidance_scale, eta, num_inference_steps): samples_path = Path(self.output_dir).joinpath("samples") unwrapped = self.accelerator.unwrap_model(self.text_encoder) scheduler = EulerAncestralDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) # Save a sample image pipeline = VlpnStableDiffusion( text_encoder=unwrapped, vae=self.vae, unet=self.unet, tokenizer=self.tokenizer, scheduler=scheduler, ).to(self.accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) train_data = self.datamodule.train_dataloader() val_data = self.datamodule.val_dataloader() generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) stable_latents = torch.randn( (self.sample_batch_size, pipeline.unet.in_channels, height // 8, width // 8), device=pipeline.device, generator=generator, ) with torch.autocast("cuda"), torch.inference_mode(): for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: all_samples = [] file_path = samples_path.joinpath(pool, f"step_{step}.png") file_path.parent.mkdir(parents=True, exist_ok=True) data_enum = enumerate(data) for i in range(self.sample_batches): batches = [batch for j, batch in data_enum if j * data.batch_size < self.sample_batch_size] prompt = [prompt.format(identifier=self.instance_identifier) for batch in batches for prompt in batch["prompts"]][:self.sample_batch_size] nprompt = [prompt for batch in batches for prompt in batch["nprompts"]][:self.sample_batch_size] samples = pipeline( prompt=prompt, negative_prompt=nprompt, height=self.sample_image_size, width=self.sample_image_size, latents_or_image=latents[:len(prompt)] if latents is not None else None, generator=generator if latents is not None else None, guidance_scale=guidance_scale, eta=eta, num_inference_steps=num_inference_steps, output_type='pil' ).images all_samples += samples del samples image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) image_grid.save(file_path) del all_samples del image_grid del unwrapped del scheduler del pipeline del generator del stable_latents if torch.cuda.is_available(): torch.cuda.empty_cache() def main(): args = parse_args() global_step_offset = 0 if args.resume_from is not None: basepath = Path(args.resume_from) print("Resuming state from %s" % args.resume_from) with open(basepath.joinpath("resume.json"), 'r') as f: state = json.load(f) global_step_offset = state["args"].get("global_step", 0) print("We've trained %d steps so far" % global_step_offset) else: now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") basepath = Path(args.output_dir).joinpath(slugify(args.placeholder_token), now) basepath.mkdir(parents=True, exist_ok=True) accelerator = Accelerator( log_with=LoggerType.TENSORBOARD, logging_dir=f"{basepath}", gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision ) logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG) # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) args.instance_identifier = args.instance_identifier.format(args.placeholder_token) # Load the tokenizer and add the placeholder token as a additional special token if args.tokenizer_name: tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) elif args.pretrained_model_name_or_path: tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer') # Convert the initializer_token, placeholder_token to ids initializer_token_ids = torch.stack([ torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1]) for token in args.initializer_token ]) num_added_tokens = tokenizer.add_tokens(args.placeholder_token) print(f"Added {num_added_tokens} new tokens.") placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) # Load models and create wrapper for stable diffusion text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder') vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae') unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder='unet') prompt_processor = PromptProcessor(tokenizer, text_encoder) unet.set_use_memory_efficient_attention_xformers(True) if args.gradient_checkpointing: text_encoder.gradient_checkpointing_enable() # slice_size = unet.config.attention_head_dim // 2 # unet.set_attention_slice(slice_size) # Resize the token embeddings as we are adding new special tokens to the tokenizer text_encoder.resize_token_embeddings(len(tokenizer)) # Initialise the newly added placeholder token with the embeddings of the initializer token token_embeds = text_encoder.get_input_embeddings().weight.data original_token_embeds = token_embeds.detach().clone().to(accelerator.device) if args.resume_checkpoint is not None: token_embeds[placeholder_token_id] = torch.load(args.resume_checkpoint)[args.placeholder_token] else: initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings): token_embeds[token_id] = embeddings # Freeze vae and unet freeze_params(vae.parameters()) freeze_params(unet.parameters()) # Freeze all parameters except for the token embeddings in text encoder params_to_freeze = itertools.chain( text_encoder.text_model.encoder.parameters(), text_encoder.text_model.final_layer_norm.parameters(), text_encoder.text_model.embeddings.position_embedding.parameters(), ) freeze_params(params_to_freeze) if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") optimizer_class = bnb.optim.AdamW8bit else: optimizer_class = torch.optim.AdamW # Initialize the optimizer optimizer = optimizer_class( text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) noise_scheduler = DDPMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=args.noise_timesteps ) def collate_fn(examples): prompts = [example["prompts"] for example in examples] nprompts = [example["nprompts"] for example in examples] input_ids = [example["instance_prompt_ids"] for example in examples] pixel_values = [example["instance_images"] for example in examples] # concat class and instance examples for prior preservation if args.num_class_images != 0 and "class_prompt_ids" in examples[0]: input_ids += [example["class_prompt_ids"] for example in examples] pixel_values += [example["class_images"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(dtype=torch.float32, memory_format=torch.contiguous_format) input_ids = prompt_processor.unify_input_ids(input_ids) batch = { "prompts": prompts, "nprompts": nprompts, "input_ids": input_ids, "pixel_values": pixel_values, } return batch datamodule = CSVDataModule( data_file=args.train_data_file, batch_size=args.train_batch_size, prompt_processor=prompt_processor, instance_identifier=args.instance_identifier, class_identifier=args.class_identifier, class_subdir="cls", num_class_images=args.num_class_images, size=args.resolution, repeats=args.repeats, center_crop=args.center_crop, valid_set_size=args.valid_set_size, num_workers=args.dataloader_num_workers, collate_fn=collate_fn ) datamodule.prepare_data() datamodule.setup() if args.num_class_images != 0: missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()] if len(missing_data) != 0: batched_data = [missing_data[i:i+args.sample_batch_size] for i in range(0, len(missing_data), args.sample_batch_size)] scheduler = EulerAncestralDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) pipeline = VlpnStableDiffusion( text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer, scheduler=scheduler, ).to(accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) with torch.autocast("cuda"), torch.inference_mode(): for batch in batched_data: image_name = [p.class_image_path for p in batch] prompt = [p.prompt.format(identifier=args.class_identifier) for p in batch] nprompt = [p.nprompt for p in batch] images = pipeline( prompt=prompt, negative_prompt=nprompt, num_inference_steps=args.sample_steps ).images for i, image in enumerate(images): image.save(image_name[i]) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() train_dataloader = datamodule.train_dataloader() val_dataloader = datamodule.val_dataloader() # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if args.lr_scheduler == "one_cycle": lr_scheduler = get_one_cycle_schedule( optimizer=optimizer, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) elif args.lr_scheduler == "cosine_with_restarts": lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, num_cycles=args.lr_cycles or math.ceil(math.sqrt( ((args.max_train_steps - args.lr_warmup_steps) / num_update_steps_per_epoch))), ) else: lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler ) # Move vae and unet to device vae.to(accelerator.device) unet.to(accelerator.device) # Keep vae and unet in eval mode as we don't train these vae.eval() unet.eval() # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch num_val_steps_per_epoch = len(val_dataloader) num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) val_steps = num_val_steps_per_epoch * num_epochs # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: config = vars(args).copy() config["initializer_token"] = " ".join(config["initializer_token"]) config["placeholder_token"] = " ".join(config["placeholder_token"]) accelerator.init_trackers("textual_inversion", config=config) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num Epochs = {num_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. global_step = 0 min_val_loss = np.inf checkpointer = Checkpointer( datamodule=datamodule, accelerator=accelerator, vae=vae, unet=unet, tokenizer=tokenizer, text_encoder=text_encoder, instance_identifier=args.instance_identifier, placeholder_token=args.placeholder_token, placeholder_token_id=placeholder_token_id, output_dir=basepath, sample_image_size=args.sample_image_size, sample_batch_size=args.sample_batch_size, sample_batches=args.sample_batches, seed=args.seed ) if accelerator.is_main_process: checkpointer.save_samples( 0, args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) local_progress_bar = tqdm( range(num_update_steps_per_epoch + num_val_steps_per_epoch), disable=not accelerator.is_local_main_process, dynamic_ncols=True ) local_progress_bar.set_description("Epoch X / Y") global_progress_bar = tqdm( range(args.max_train_steps + val_steps), disable=not accelerator.is_local_main_process, dynamic_ncols=True ) global_progress_bar.set_description("Total progress") try: for epoch in range(num_epochs): local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") local_progress_bar.reset() text_encoder.train() train_loss = 0.0 sample_checkpoint = False for step, batch in enumerate(train_dataloader): with accelerator.accumulate(text_encoder): # Convert images to latent space latents = vae.encode(batch["pixel_values"]).latent_dist.sample() latents = latents * 0.18215 # Sample noise that we'll add to the latents noise = torch.randn(latents.shape).to(latents.device) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"]) # Predict the noise residual noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample if args.num_class_images != 0: # Chunk the noise and noise_pred into two parts and compute the loss on each part separately. noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0) noise, noise_prior = torch.chunk(noise, 2, dim=0) # Compute instance loss loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() # Compute prior loss prior_loss = F.mse_loss(noise_pred_prior, noise_prior, reduction="none").mean([1, 2, 3]).mean() # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss else: loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() accelerator.backward(loss) # Keep the token embeddings fixed except the newly added # embeddings for the concept, as we only want to optimize the concept embeddings if accelerator.num_processes > 1: token_embeds = text_encoder.module.get_input_embeddings().weight else: token_embeds = text_encoder.get_input_embeddings().weight # Get the index for tokens that we want to freeze index_fixed_tokens = torch.arange(len(tokenizer)) != placeholder_token_id token_embeds.data[index_fixed_tokens, :] = original_token_embeds[index_fixed_tokens, :] optimizer.step() if not accelerator.optimizer_step_was_skipped: lr_scheduler.step() optimizer.zero_grad(set_to_none=True) loss = loss.detach().item() train_loss += loss # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: local_progress_bar.update(1) global_progress_bar.update(1) global_step += 1 if global_step % args.sample_frequency == 0: sample_checkpoint = True if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process: local_progress_bar.clear() global_progress_bar.clear() checkpointer.checkpoint(global_step + global_step_offset, "training") save_args(basepath, args, { "global_step": global_step + global_step_offset, "resume_checkpoint": f"{basepath}/checkpoints/last.bin" }) logs = {"train/loss": loss, "lr": lr_scheduler.get_last_lr()[0]} accelerator.log(logs, step=global_step) local_progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break train_loss /= len(train_dataloader) accelerator.wait_for_everyone() text_encoder.eval() val_loss = 0.0 with torch.autocast("cuda"), torch.inference_mode(): for step, batch in enumerate(val_dataloader): latents = vae.encode(batch["pixel_values"]).latent_dist.sample() latents = latents * 0.18215 noise = torch.randn(latents.shape).to(latents.device) bsz = latents.shape[0] timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"]) noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise)) loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() loss = loss.detach().item() val_loss += loss if accelerator.sync_gradients: local_progress_bar.update(1) global_progress_bar.update(1) logs = {"val/loss": loss} local_progress_bar.set_postfix(**logs) val_loss /= len(val_dataloader) accelerator.log({"val/loss": val_loss}, step=global_step) local_progress_bar.clear() global_progress_bar.clear() if min_val_loss > val_loss: accelerator.print( f"Global step {global_step}: Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}") checkpointer.checkpoint(global_step + global_step_offset, "milestone") min_val_loss = val_loss if sample_checkpoint and accelerator.is_main_process: checkpointer.save_samples( global_step + global_step_offset, args.resolution, args.resolution, 7.5, 0.0, args.sample_steps) # Create the pipeline using using the trained modules and save it. if accelerator.is_main_process: print("Finished! Saving final checkpoint and resume state.") checkpointer.checkpoint(global_step + global_step_offset, "end") save_args(basepath, args, { "global_step": global_step + global_step_offset, "resume_checkpoint": f"{basepath}/checkpoints/last.bin" }) accelerator.end_training() except KeyboardInterrupt: if accelerator.is_main_process: print("Interrupted, saving checkpoint and resume state...") checkpointer.checkpoint(global_step + global_step_offset, "end") save_args(basepath, args, { "global_step": global_step + global_step_offset, "resume_checkpoint": f"{basepath}/checkpoints/last.bin" }) accelerator.end_training() quit() if __name__ == "__main__": main()